The impact of inpatient bloodstream infections caused by antibiotic-resistant bacteria in low- and middle-income countries: A systematic review and meta-analysis

Background Bloodstream infections (BSIs) produced by antibiotic-resistant bacteria (ARB) cause a substantial disease burden worldwide. However, most estimates come from high-income settings and thus are not globally representative. This study quantifies the excess mortality, length of hospital stay (LOS), intensive care unit (ICU) admission, and economic costs associated with ARB BSIs, compared to antibiotic-sensitive bacteria (ASB), among adult inpatients in low- and middle-income countries (LMICs). Methods and findings We conducted a systematic review by searching 4 medical databases (PubMed, SCIELO, Scopus, and WHO’s Global Index Medicus; initial search n = 13,012 from their inception to August 1, 2022). We only included quantitative studies. Our final sample consisted of n = 109 articles, excluding studies from high-income countries, without our outcomes of interest, or without a clear source of bloodstream infection. Crude mortality, ICU admission, and LOS were meta-analysed using the inverse variance heterogeneity model for the general and subgroup analyses including bacterial Gram type, family, and resistance type. For economic costs, direct medical costs per bed-day were sourced from WHO-CHOICE. Mortality costs were estimated based on productivity loss from years of potential life lost due to premature mortality. All costs were in 2020 USD. We assessed studies’ quality and risk of publication bias using the MASTER framework. Multivariable meta-regressions were employed for the mortality and ICU admission outcomes only. Most included studies showed a significant increase in crude mortality (odds ratio (OR) 1.58, 95% CI [1.35 to 1.80], p < 0.001), total LOS (standardised mean difference “SMD” 0.49, 95% CI [0.20 to 0.78], p < 0.001), and ICU admission (OR 1.96, 95% CI [1.56 to 2.47], p < 0.001) for ARB versus ASB BSIs. Studies analysing Enterobacteriaceae, Acinetobacter baumanii, and Staphylococcus aureus in upper-middle-income countries from the African and Western Pacific regions showed the highest excess mortality, LOS, and ICU admission for ARB versus ASB BSIs per patient. Multivariable meta-regressions indicated that patients with resistant Acinetobacter baumanii BSIs had higher mortality odds when comparing ARB versus ASB BSI patients (OR 1.67, 95% CI [1.18 to 2.36], p 0.004). Excess direct medical costs were estimated at $12,442 (95% CI [$6,693 to $18,191]) for ARB versus ASB BSI per patient, with an average cost of $41,103 (95% CI [$30,931 to $51,274]) due to premature mortality. Limitations included the poor quality of some of the reviewed studies regarding the high risk of selective sampling or failure to adequately account for relevant confounders. Conclusions We provide an overview of the impact ARB BSIs in limited resource settings derived from the existing literature. Drug resistance was associated with a substantial disease and economic burden in LMICs. Although, our results show wide heterogeneity between WHO regions, income groups, and pathogen–drug combinations. Overall, there is a paucity of BSI data from LMICs, which hinders implementation of country-specific policies and tracking of health progress.


Search strategy
We searched the literature for studies examining the burden of ARB BSIs compared with ASB BSIs among inpatients from LMICs. PubMed, SCIELO, Scopus, and WHO's Global Index Medicus (Latin American and Caribbean Health Sciences Literature "LILACs" and African Index Medicus "AIM") were searched without restrictions to language or year of publication using a family of keywords related to antibiotic/drug-resistance, bloodstream infections/bacteraemia, and burden measures among inpatients. We searched articles published through August 1, 2022. The complete list of terms, abbreviations, and Boolean connectors used by search engine can be found in the Supporting information (S1 Text, section 1).

Study selection
We selected articles according to a step-guided protocol. First, articles were excluded if carried out in high-income countries; these were defined according to the 2021 World Bank classification list (i.e., gross national income "GNI" per capita > $12,696) [19]. Second, studies were only included if BSIs were presented based on laboratory-confirmed positive blood cultures. Either primary or secondary BSIs were included. Articles that analysed patients with different culture types (e.g., blood, urine, wound, nasal) were removed unless BSI episodes were clearly detailed. Third, articles were included if the ASB and ARB groups were identified among adult patients presenting BSIs in the hospital. Fourth, participants with chronic or severe diseases (e.g., HIV, cancer) were removed unless they were present in the ARB and ASB groups (e.g., studies were withdrawn if HIV-positive patients having ARB BSIs were compared with HIVnegative patients having ASB BSIs). Finally, studies were removed if they did not present our selected outcomes (i.e., mortality, ICU admission, LOS, or costs). Experimental and observational articles were included. We removed correspondence letters or opinions, short reports without data analysis, literature reviews, and single-case studies.
Studies were analysed only when the number of patients was reported. We only included the adult population (average �18 years of age) because (i) the number of studies focusing on children was limited (n = 4) after looking at the provisional results; and (ii) children's inherent behaviour and exposure level differ from adults [3]. Only data on WHO-priority pathogens were retained [20]. The Results section (PRISMA chart) and Table A in S1 Text present the complete list of search criteria used.
To avoid our study hinging only on published articles' results, we systematically reviewed the grey literature and other current literature reviews analysing similar topics. Four referees resolved any disagreement presented at any stage of study selection through scholarly discussion. Two native Spanish speakers fluent in Portuguese and English, a native English speaker, and a native Chinese speaker fluent in English conducted the screening and consecutive data extraction. Papers written in any other language were translated to English using Google Translate PDF (<1% of the included articles). We used the Rayyan free online tool (https:// rayyan.ai/) to screen, select, and decide which articles were included. Double article screening for eligibility was employed, and discrepancies were resolved via scholarly dialogue.
control group comprised sensitive-strain infections (ASB). Selected studies were organised using unique identifiers (e.g., 1, 2, 3), and sub-studies within the primary articles were classified using consecutive numbers separated by a dot (e.g., 1.1, 1.2, 1.3) if they presented bacterium-or resistance type-specific information (S1 Data).
We extracted the following outcomes by case/control group: mortality (crude 30-day mortality, whenever available, or overall crude mortality if timing was not reported), LOS (average total days and standard deviation), and ICU admission (patients admitted). We also collected data on demographics and underlying conditions: average age, previous surgery and hospitalisation, community-or hospital-acquired BSI, any underlying condition (diabetes, hypertension, cardiovascular or heart diseases, solid tumour or malignancy, liver or kidney disease, pulmonary/respiratory diseases, and any hematologic disease), and BSI source (urinary tract, intravenous or catheter, pulmonary, and intrabdominal or gastrointestinal). Pitt bacteraemia score, APACHE II, and CHARLSON scores were collected if presented. We compared ARB and ASB groups by comparing variables' proportion or mean using McNemar's χ 2 or T-tests for binary and continuous data, respectively. Additionally, we classified the studies by World Bank income level, WHO region, WHO Global Priority Pathogens List, bacterium family and antibiotic class, pathogen strain, and bacterium Gram type. We used Microsoft Excel 2022 to compile and extract included articles' data. We used double data extraction reviewing, and inconsistencies (14% disagreement) were resolved through scholarly discussion.

Study quality and risk assessment
We used a unified framework to evaluate the methodological quality of analytic study designs (MASTER scale) [21]. This framework comprises 36 questions classified into 7 domains concerning equal recruitment, retention, implementation, prognosis, ascertainment, sufficient analysis, and temporal precedence. Each question was scored independently by 2 reviewers as 1 if the study complied with the domain or 0 if it did not. Therefore, a higher score indicates higher study quality. Two independent reviewers performed a risk of bias assessment. Conflicts were addressed through scholarly discussion.

Statistical analysis
Firstly, we employed population-weighted descriptive statistics of the health and demographic characteristics collated by studies' patients having ARB and ASB BSIs to contrast both groups and check whether mean differences across patient features existed. Secondly, the overall estimates for excess mortality, ICU admission, and LOS associated with resistant strains compared to their sensitive counterparts were meta-analysed using the inverse variance heterogeneity model [22]. The heterogeneity was calculated using the I 2 statistics; I 2 values were classified as high (>75%), moderate (50% to 75%), and low (<50%) heterogeneity. All results were computed using odds ratios (ORs) for mortality and ICU admission rates, and the standardised mean difference (SMD) for LOS. We estimated ORs based on studies' crude numbers or unadjusted ORs provided. Forest plots and meta-analyses were computed by outcome and subgroups of variables, including bacterial family, Gram type, reported resistance type, most common antibiotic-resistant microbial strains, World Bank income group, and WHO region. P-values (p) were reported using a two-tailed t test (p < 0.05) for the ORs for mortality and ICU admissions and LOS's standardised mean difference. We also analysed and compared, whenever reported, the unadjusted and confounder-adjusted ORs, for studies reporting univariate and multivariable regression analyses.
As a secondary analysis, we used univariate and multivariable meta-regressions to explore the main determinants of mortality and ICU admission (LOS was not included because of a small sample size). We included the bacterial family and resistance profile, demographics, and underlying health condition variables in the univariate regression. Variables were transformed to odds between ARB and ASB groups. We evaluated the associations with the original and fully imputed observations. Multiple imputations were performed using fully completed data as factors and with 1,000 repetitions following a multivariable normal regression design. Variables associated with our outcomes in the univariate analysis with p < 0.05 using non-imputed data were included in the fully imputed multivariable model.
Excess economic costs per patient (i.e., costs associated with ARB BSI minus costs associated with ASB BSI) were computed only for excess length of stay, separated by ICU and non-ICU wards. Hospital-day costs included all the inpatient hospitality costs per patient stay for primary and secondary level and teaching hospitals and were calculated based on WHO--CHOICE costs [23]. ICU costs were calculated per patient stay for tertiary/teaching hospitals and were retrieved from the literature for countries with available information [24-36], or by using an approximation ratio between hospital and ICU costs [37][38][39]. Direct medical costs comprised hospital-day and ICU admission costs per patient, adjusted to their respective patients' LOS in the hospitalised or ICU services. We also calculated excess productivity losses per patient associated with premature mortality from ARB BSIs (compared to ASB BSIs) using the life expectancy at death and human capital approaches [40]. Excess productivity losses associated with premature mortality costs were computed by multiplying the years of life lost, based on the reference standard life expectancy at the average age of death [41] from ARB BSI (i.e., costs associated with ARB BSI minus costs associated with ASB BSI), using the studyweighted average age for all patients over all studies, without age-weights and a 5% time discount [42]. All costs were expressed in 2020 USDs, adjusting for inflation using US GDP implicit price deflators. Due to a lack of data, we excluded direct and indirect nonmedical costs (e.g., travel). Cost computations and methods are detailed in S1 Text, section 4.

Small-study effects
The Doi [43] plots and the LFK index were used to evaluate small-study effects when there were at least 5 studies in the meta-analysis. Leave-one-out cross-validation [44] was used to estimate the generalisation performance of our main meta-analyses to cross-validate the results' sensitivity.

Sensitivity analyses
We evaluated whether our main meta-analysis results varied by location. Due to the large proportion of studies from China (N = 41), we assessed our meta-analyses by separating our sampled studies into those performed in China and other LMICs.
All statistical analyses included studies and sub-studies according to their specific population features and were performed in Stata 17, College Station, TX: StataCorp LLC.

PLOS MEDICINE
The impact of bloodstream infections caused by antimicrobial resistance in LMICs  (Fig 3). The main gram-positive pathogens reported were Staphylococcus aureus (19.3%; 21/109) and Enterococcus spp. (7.3%; 8/109); 75.2% (82/109) of the pathogens reported were classified as a critical priority following the WHO criteria (Fig 3). β-lactam antibiotics were among the most tested antibiotic class within the studies (67.9%; 74/109), 71.6% (53/74) of which were carbapenems or cephalosporins (Fig 3). The total number of patients and most prevalent features per country's studies are reported in Table E in S1 Text. Table F in S1 Text presents the weighted unadjusted differences for sociodemographic and health variables among ARB and ASB groups. We found no statistically significant difference between ARB and ASB groups for most of these variables (χ 2 test p > 0.05). S1 Text section 2 describes the distribution of our studies by WHO region, World Bank income group, year, and outcomes densities per ARB/ASB group.

Quantitative results
The odds of health outcomes.    Enterococcus spp. stands for Enterococcus species pluralis (multiple species), which included Enterococcus faecalis and faecium. The multiple categories stand for either multiple bacteria or antibiotics analysed throughout our selected studies, which were not reported disaggregated by bacterial family, biological strain, gram type, or WHO priority pathogen list. † Studies could include more than 1 subcategory per biological feature (i.e., a study might report Enterobacteriaceae and Pseudomonadaceae species separately in their analyses, or altogether, in which case it was classified as "Multiple," meaning no clear distinction between subcategories). Categories might not be exclusive per study. WHO, World Health Organization. https://doi.org/10.1371/journal.pmed.1004199.g003

Estimated excess costs
The average excess hospital bed-days cost per ARB BSI patient in tertiary/teaching hospitals, adjusted by the calculated excess LOS from Table 2 and excluding drugs and tests costs, was $812.5 (95% CI [$331.6 to $1,293.3]) (Table J in S1 Text). The excess costs per patient varied considerably between countries, ranging from $30.9, $95.9, and $131.7 (Ethiopia, Pakistan, and India, respectively) to $1,681.7 and $1,683.2 (Mexico and Turkey) (Fig 4, panel A).
We estimated an average excess of productivity loss (indirect costs associated with ARB BSI for an average patient) from years of potential life lost due to premature mortality of $41,102 (95% CI = $30,931 to $51,274) for all bacteria combined (Table L in S1 Text). Romania presented the highest excess producitivity lossess attributed to years of potential life-lost costs per patient, while Ethiopia had the lowest ($86,217 and $6,070, respectively). Mortality costs due to premature mortality using the life expectancy approach had an observed average of $132,560 per patient (95% CI [$99,753 to $165,363]) among all sampled countries (  $67,251]). Excess costs for ICU adjusted to ICU's length of stay were 14 times higher compared with hospital-bed LOS-adjusted among patients with ARB BSIs. Lower middle-income countries had the lowest economic burdens per patient; however, we found substantial between-country differences.

PLOS MEDICINE
The impact of bloodstream infections caused by antimicrobial resistance in LMICs Full details on cost calculation can be found in S1 Text, section 4.

Quality and risk assessment
Using the MASTER scale for methodological assessment, we calculated, on average, 25.1, 23.7, and 23.6 points (out of 36) for the mortality, ICU admission, and length of hospital stay outcomes, respectively (Table 4). Our scores reflect that few studies addressed key confounders (e.g., using statistical methods to control for other correlated risk factors) to account for different prognoses and equal ascertainment (especially for participants, analysts, and caregivers' blindness towards evaluation; <2% of included studies). Only 37%, 11%, and 13% of the studies incorporated statistical techniques (e.g., regression analyses, stratification, matching, among others) for an equal prognosis for the mortality, ICU admission, and LOS outcomes, respectively (Table 4, equal prognosis scores). Most studies achieved equal retention (e.g., low missing data and null attrition) and sufficient analyses safeguards (e.g., absence of numerical contradictions and data dredging), regardless of the outcome analysed. Full results are found in S1 Text sections 8 and 9 and S1 Data, Master Scale spreadsheet.

Sensitivity analyses
General mortality estimates from studies in China were not different from studies conducted elsewhere. However, we found larger disaggregated estimates for subgroup meta-analyses, such as Enterobacteriaceae, Moraxellaceae, Pseudomonaceae, and Staphylococcaceae species (8%, 25%, 26%, and 20%, respectively) compared to the average mortality estimates reported in Table 2 for the same subgroups. General LOS SMD was 16% higher among countries other than China, compared to the estimates reported in Table 2, specifically driven by Moraxellaceae and Staphylococcaceae species. Finally, the odds for excess ICU admission were 25% greater in China, with respect to average ICU admission found in all included studies, driven by 27% elevated odds among patients having BSIs caused by gram-negative bacteria. Full results in Tables U and V in S1 Text.
When applying the leave-one-out method to our meta-analyses, we observed that after assessing the effect of every single study on the overall estimates, the numbers presented a relative variation with respect to overall estimates ranging between −2% and 4% for mortality (OR 95% CI [1.57 to 1.58]), −8% and 4% for ICU admission (OR 95% CI [1.95 to 1.97]), and −10% and 4% for LOS (SMD 95% CI [0.48 to 0.50]) (S1 Text, section 6). These results suggest a moderate influence of our studies in the overall estimates if relative variations are compared, especially for ICU admission and LOS.

Discussion
Antibiotic resistance imposes substantial morbidity, mortality, and societal costs in LMICs [153]. Bloodstream infections with ARB are among the most lethal, imposing a large disease burden. Examining all available data for hospitalised patients in LMICs, we found that ARB BSIs with WHO critical-and high-priority pathogens were associated with increased mortality Our findings on mortality are consistent with the recent estimates by the Global Burden of Disease study [154]. The largest mortality impact was associated with resistant A. baumannii and Enterobacteriaceae. Both bacteria featured in the global top 5 contributors to resistanceassociated and -attributable deaths in 2019 [154]. Between a quarter and half of the patients with ARB BSIs caused by Enterobacteriaceae, A. baumannii or P. aureginosa die, corroborating findings from different country settings for Enterobacteriaceae [8,67], P. aeruginosa [155], and large university hospitals in Israel and the US for A. baumanii [156,157].
Our results suggest that patients who acquired ARB BSIs during their hospital stay had an overall hospital stay that is about a week longer than patients that acquired ASB BSIs. However, in our study, we could not distinguish between excess length of stay before or after BSI, and as such this is likely an overestimation. Depending on the pathogen, resistant infections have previously been shown to increase LOS typically by 2.0 to 12.7 days [158]. Longer hospital stay, especially before BSI onset, is a primary risk factor for acquiring a resistant infection due to the cumulative risk of hospital transmission of ARBs [158,159]. We found that MRSA had the greatest impact on LOS (extending stay by 14 days relative to sensitive S. aureus). Others have also shown considerably increased LOS as a result of MRSA compared with sensitive S. aureus: Tsuzuki and colleagues [160] showed an excess overall LOS and LOS after BSI onset of 20 and 7 days, respectively; similarly, Graffunder and colleagues [161] showed MRSA patients presented an overall LOS of 3 weeks longer. Resistant infections are more difficult to treat and increase the rate of ICU admissions. Our analysis showed that resistant Enterobacteriaceae infections more than doubled the odds of ICU admission. This finding is comparable with the 2.69 higher odds of ICU admission previously shown among patients with carbapenem-resistant K. pneumoniae BSIs [162]. Our exploratory analysis for studies performed in China and LMICs other than China exhibited divergent results. We found that China's patients with antibiotic-resistant gram-negative BSIs (A. baumanii, Enterobacteriaceae, and P. aeruginosa) displayed higher excess mortality, ICU admission, and LOS, compared to the other LMICs with reported data. Large increases in antibiotic consumption and resistance levels over the last 20 years and the rapid development or acquisition of drug resistance among gram-negative pathogens might explain the greater excess mortality and morbidity for ARB BSIs in China [1,163,164]. Correspondingly, inappropriate administration of empirical treatments and low testing rates could increase the burden outcomes for patients with ARB BSIs in these settings [165].
Despite being fundamental to resource allocation for healthcare provision, we found very little data on excess costs associated with ARB BSIs among the reviewed studies. One study conducted in Thailand, reported excess costs associated with hospital-acquired carbapenemresistant A. baumannii of $5,682 [61]. A study conducted in Colombia, reported excess  [154]: there is a sparsity of data on ARB from LMICs. Only 18 of the 137 (13%) LMICs published any AMR outcome study. Consistent antibiotic resistance surveillance puts demands on clinical bacteriology, quality control, and data linkage between culture test results and clinical outcomes, which is beyond the capabilities of many LMICs. Applying the leave-one-out method to our meta-analyses (S1 Text, section 6) showed a minor-to-moderate influence of individual studies likely due to the heterogeneity in clinical settings, indicating that our model's results are robust (assuming countries' missing information and selection biases are heterogeneously distributed). Future efforts to improve coverage should prioritise WHO's Africa region, where data were remarkably absent, with no estimates for resistance-associated LOS or ICU admissions. Our results indicate that the studies from the Western Pacific and European areas show the highest excess mortality from ARB BSIs. Studies from Africa show among the lowest but this region has limited data and substantial uncertainty; it is essential to improve epidemiological surveillance of ARB BSIs in this region in particular [169]. Second, some articles were of low quality or reported limited data. Studies often failed to account for confounding factors; hence our analyses relied upon crude estimates. ARB surveillance networks vary in blood culture sampling, potentially overestimating the number of severe cases if selective sampling among patients fulfilling the case definition is present. Third, we did not estimate the total relative harm of ARB BSIs relative to where such infections were prevented (compared to non-infected patients) [170], primarily because of the limited number of studies [171]. While we accounted for some key risk factors when comparing antibiotic-sensitive and antibiotic-resistant groups in the metaregression, others were unavailable. We could not match comparison groups by factors known to impact patients' underlying health conditions, such as illness severity, prolonged previous hospital stays, or the use of invasive devices. The reported LOS does not distinguish between total LOS and LOS following BSI infection, thus risking reverse causality [172]. This ecological study was designed to identify associations; consequently, our results should be interpreted cautiously. Also, we adjusted WHO-CHOICE country estimates using US GPD implicit price deflators, which may not necessarily reflect price changes in some LMICs, particularly for non-tradable cost components of healthcare. Finally, we may have overestimated the true effect size of the association between ARB BSIs and mortality as indicated by the exploratory analysis of studies' adjusted-compared to unadjusted-ORs reporting both estimates, specifically among gramnegative species.
Here, we described an updated evaluation of the health impact and excess economic costs of resistant BSIs in low-resourced settings. Our results highlight regions where improved surveillance, expanding microbiology laboratory capacity, and data collection systems are most needed and where the current evidence indicates WHO critical and high-priority drug-resistant pathogens exert the greatest toll on morbidity and mortality.
Supporting information S1 Text. Supporting text, tables, and figures. Text A. Search criteria used by search engine. Table A. Studies inclusion and exclusion criteria. Table B. Years of the studies included. Table C. Number of studies included by WHO region and WB income group. Table D. Correlation between main outcomes and demographic variables. Table E. Most prevalent bacterium family, Gram type, resistance type, and antibiotic-bacterium pair by country among the included studies. Table F. Descriptive statistics of the studies included in the meta-analysis. Table G. Summary of the subgroup meta-analysis results for income level and WHO region by outcome variable. Table H. Costs of hospital bed-day per patient and by country and hospital level (in 2008 USDs). Table I. Costs of total excess hospital bed-days per patient by country and hospital level using estimated SMD and their respective 95% CIs (in 2008 USDs). Table J.
Costs of total excess hospital bed-days per patient and by country and hospital level using estimated SMD and their respective 95% CIs (inflated to 2020 USDs). Table K. Calculation of YPLL, YPPLL, and CPL, by country. Table L. Total productivity losses due to premature mortality costs by country using the LE at the age of death and productivity cost approach (age of retirement), discounted. Table M. Intensive care unit costs per patient (daily). Table N. Intensive care unit costs (per patient and daily) adjusted to 2020 USDs (inflated accordingly). Table O. Intensive care unit costs (per day/patient) adjusted to ICU LOS and reported in 2020 USDs (inflated accordingly). Table P. Total excess costs incurred for bloodstream infections caused by antibiotic-resistant bacteria, per patient. Table Q. Statistics calculated for meta-analysis using mortality as an outcome, by model. Table R. Statistics calculated for meta-analysis using ICU admission as an outcome, by model. Table S. Statistics calculated for meta-analysis using the length of stay at hospital as an outcome, by model. Table T. Summary of the subgroup meta-analysis results for specific antibiotic-bacterium combinations declared important by the WHO, by outcome variable. Table U. Meta-analysis subgroup results for bacterium family, and Gram type for those studies carried out in China and other than China, by outcome. Table V. Summary results of meta-analysis results for critical antibiotic-bacterium pathogens for those studies in China and other than China, by outcome.  . Fig A. Density of the studies over time. Fig B. Violin and kernel density estimate plots for the main outcomes and by ARB susceptibility. Fig C. Relationship between the main outcomes. Fig D. Meta-analysis using all the studies reporting mortality rates. Fig E. Subgroup meta-analysis using all the studies reporting mortality rates/odds for critical (N = 72) and high-priority (N = 22) pathogens according to the WHO criteria. Fig F. Subgroup meta-analysis using all the studies reporting mortality rates by bacterium's family name. Fig G. Subgroup meta-analysis using all the studies reporting mortality rates by WHO Region. Fig H. Subgroup meta-analysis using all the studies reporting mortality rates by income level . Fig I. Meta-analysis results using all the studies reporting the mean and SD for the length of stay at the hospital. Fig J. Subgroup meta-analysis using all the studies reporting the mean and SD for the length of stay at the hospital for critical and high-priority pathogens according to the WHO. Fig K. Subgroup meta-analysis using all the studies reporting the mean and SD for the length of stay at the hospital for Enterococcus spp., Enterobacteriaceae, Moraxellaceae, Pseudomonadaceae, and Staphyloccocaceae. Fig L. Subgroup meta-analysis using all the studies reporting the mean and SD for the length of stay at the hospital by income level . Fig M. Subgroup meta-analysis using all the studies reporting the mean and SD for the length of stay at the hospital by WHO region . Fig N. Meta-analysis results using all the studies reporting ICU admission rates. Fig O. Subgroup meta-analysis using all the studies reporting ICU admission rates for critical pathogens according to the WHO criteria. Fig P. Subgroup meta-analysis using all the studies reporting ICU admission rates for high-priority pathogens according to the WHO criteria. Fig  Q. Subgroup meta-analysis using all the studies reporting ICU admission rates for Enterobacteriaceae. Fig R. Subgroup meta-analysis using all the studies reporting ICU admission rates for Enterobacteriaceae.